• Journal of the Korean Operations Research and Management Science Society
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• v.20 no.2
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• pp.139-158
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• 1995

A temporal database systems provides timing information and maintains history of data compared to the conventional database system. In this paper, we present a temporal relational database which use an interval-stamping method for instant-based events and for interval-based states. A set of temporal algebraic operators are developed on an instance of time and interval of time so that we can manipulate events and states at a same time. The set of operation is the basis for creating a relational algebra that is closed for temporal relations. And temporal SQL is also suggested as a temporal query relational language for our algebraic operations on temporal relations.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.2029-2032
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• 2004

The quantity of data were rapidly increased recently and caused the data overwhelming. This led to be difficult in searching the required data. The method of eliminating redundant data was needed. One of the efficient methods was Knowledge Discovery in Database (KDD). Generally data can be separate into 2 cases, continuous data and discrete data. This paper describes algorithm that transforms continuous attributes into discrete ones. We present an Improved Class Attribute Interdependence Maximization (ICAIM), which designed to work with supervised data, for discretized process. The algorithm does not require user to predefine the number of intervals. ICAIM improved CAIM by using significant test to determine which interval should be merged to one interval. Our goal is to generate a minimal number of discrete intervals and improve accuracy for classified class. We used iris plant dataset (IRIS) to test this algorithm compare with CAIM algorithm.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.1
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• pp.31-36
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• 2008

This paper presents adaptive learning data of evolvable neural networks (ENNs) for time series prediction of nonlinear dynamic systems. ENNs are a special class of neural networks that adopt the concept of biological evolution as a mechanism of adaptation or learning. ENNs can adapt to an environment as well as changes in the enviromuent. ENNs used in this paper are L-system and DNA coding based ENNs. The ENNs adopt the evolution of simultaneous network architecture and weights using indirect encoding. In general just previous data are used for training the predictor that predicts future data. However the characteristics of data and appropriate size of learning data are usually unknown. Therefore we propose adaptive change of learning data size to predict the future data effectively. In order to verify the effectiveness of our scheme, we apply it to chaotic time series predictions of Mackey-Glass data.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.920-924
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• 2005

This paper presents adaptive learning data of evolvable neural networks (ENNs) for time series prediction of nonlinear dynamic systems. ENNs are a special class of neural networks that adopt the concept of biological evolution as a mechanism of adaptation or learning. ENNs can adapt to an environment as well as changes in the environment. ENNs used in this paper are L-system and DNA coding based ENNs. The ENNs adopt the evolution of simultaneous network architecture and weights using indirect encoding. In general just previous data are used for training the predictor that predicts future data. However the characteristics of data and appropriate size of learning data are usually unknown. Therefore we propose adaptive change of learning data size to predict the future data effectively. In order to verify the effectiveness of our scheme, we apply it to chaotic time series predictions of Mackey-Glass data.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.11a
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• pp.444-447
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• 2004

A heart diagnosis system adopts hundreds of Superconducting Quantum Interface Device(SQUID) sensors for precision MCG(Magnetocardiogram) or MEG(Magnetoencephalogram) signal acquisitions. This system requires correct and real-time data acquisition from the sensors in a required sampling interval, i.e., 1 mili-second. This paper presents our hardware design and test results, to acquire data from 256 channel analog signal with 1-ksample/sec speed, using 12-bit 8-channel ADC devices, SPI interfaces, parallel interfaces, and 8-bit microprocessors. We chose to implement parallel data transfer between microprocessors as an effective way of achieving such data collection. Our result concludes that the data collection can be done in 250 ${\mu}sec$ time-interval.

• Journal of Korean Neurosurgical Society
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• v.59 no.6
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• pp.590-596
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• 2016

Objective : Rheumatoid arthritis (RA) is a systemic disease that can affect the cervical spine, especially the atlantoaxial region. The present study evaluated the risk factors for atlantoaxial subluxation (AAS) development and progression in patients who have undergone surgical treatment. Methods : We retrospectively analyzed the data of 62 patients with RA and surgically treated AAS between 2002 and 2015. Additionally, we identified 62 patients as controls using propensity score matching of sex and age among 12667 RA patients from a rheumatology registry between 2007 and 2015. We extracted patient data, including sex, age at diagnosis, age at surgery, disease duration, radiographic hand joint changes, and history of methotrexate use, and laboratory data, including presence of rheumatoid factor and the C-reactive protein (CRP) level. Results : The mean patient age at diagnosis was 38.0 years. The mean time interval between RA diagnosis and AAS surgery was $13.6{\pm}7.0$ years. The risk factors for surgically treated AAS development were the serum CRP level (p=0.005) and radiographic hand joint erosion (p=0.009). The risk factors for AAS progression were a short time interval between RA diagnosis and radiographic hand joint erosion (p<0.001) and young age at RA diagnosis (p=0.04). Conclusion : The CRP level at RA diagnosis and a short time interval between RA diagnosis and radiographic hand joint erosion might be risk factors for surgically treated AAS development in RA patients. Additionally, a short time interval between RA diagnosis and radiographic hand joint erosion and young age at RA diagnosis might be risk factors for AAS progression.

• Journal of the Korean Data and Information Science Society
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• v.24 no.4
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• pp.825-833
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• 2013

An estimation of confidence intervals is essential to calculate uncertainty for greenhouse gases inventory. It is generally assumed that the population has a normal distribution for the confidence interval of parameters. However, in case data distribution is asymmetric, like nonnormal distribution or positively skewness distribution, the traditional estimation method of confidence intervals is not adequate. This study compares two estimation methods of confidence interval; parametric and non-parametric method for exponential distribution as an asymmetric distribution. In simulation study, coverage probability, confidence interval length, and relative bias for the evaluation of the computed confidence intervals. As a result, the chi-square method and the standardized t-bootstrap method are better methods in parametric methods and non-parametric methods respectively.

• Journal of the Institute of Convergence Signal Processing
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• v.15 no.1
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• pp.9-14
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• 2014

The path loss data was re-calculated according to the distance between the base station and a mobile station in the mobile telecommunications network. In this paper, the averaged path loss data was plotted with the conventional path loss models(free space, plane earth, Hata model ${\ldots}$). The standard deviations for the 2 Km, 1 Km, 0.5 Km-interval averaged path loss were 2.29 dB, 3.39 dB, 4.75 dB, respectively. Additionally, the derivative values for the 2 Km, 1 Km, 0.5 Km-interval averaged path loss were evaluated to find the positions with more than 1 times or 2times of the standard deviation. The situations with the sharply fluctuated path loss were calculated to 5 positions in the 2 Km interval, to 7 positions in the 1 Km interval, to 19 positions in the 0.5 Km interval, respectively. And, the exact distances between the base station and a mobile station were found with the sharply fluctuated path loss.

• The KIPS Transactions:PartD
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• v.16D no.5
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• pp.691-700
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• 2009

An event means a flow which has a time attribute such as the a symptom of patients, an interval event has the time period between the start-time-point and the end-time-point. Although there are many studies for temporal data mining, they do not deal with discovering knowledge from interval event such as patient histories and purchase histories. In this paper, we suggest a method of temporal data mining that finds association rules of event causal relationships and predicts an occurrence of effect event based on discovered rules. Our method can predict the occurrence of an event by summarizing an interval event using the time attribute of an event and finding the causal relationship of event. As a result of simulation, this method can discover better knowledge than others by considering a lot of supports of an event and finding the significant rare relation on interval events which means an essential cause of an event, regardless of an occurrence support of an event in comparison with conventional data mining techniques.

• The KIPS Transactions:PartD
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• v.16D no.4
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• pp.507-516
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• 2009

In stream data processing system, generally the interval queries are in advance registered in the system. When a data is input to the system continuously, for realtime processing, a query indexing method is used to quickly search queries. Thus, a main memory-based query index with a small storage cost and a fast search time is needed for searching queries. In this paper, we propose a LVC-based(Limited Virtual Construct-based) query index method using a hashing to meet the both needs. In LVC-based query index, we divide the range of a stream into limited virtual construct, or LVC. We map each interval query to its corresponding LVC and the query ID is stored on each LVC. We have compared with the CEI-based query indexing method through the simulation experiment. When the range of values of input stream is broad and there are many short interval queries, the LVC-based indexing method have shown the performance enhancement for the storage cost and search time.